GLOBAL ROBUST STABILITY ANALYSIS FOR NEURAL NETWORKS WITH CONSTANT OR VARIABLE DELAYS

In this paper, some criteria are derived for global robust asymptotic stability of a class of interval neural networks with multiple constant or time-varying delays via the Lyapunov–Krasovskii stability theory for functional differential equations and the linear matrix inequality (LMI) approach. The main results are applied to the uncertain systems with single delay, and several new criteria to determine the asymptotical convergence of these systems are obtained. Numerical examples are also given to show the effectiveness of our results.

DELAY-DEPENDENT GLOBAL ROBUST ASYMPTOTIC STABILITY ANALYSIS OF BAM NEURAL NETWORKS WITH TIME DELAY: AN LMI APPROACH

The global robust asymptotic stability of bi-directional associative memory (BAM) neural networks with constant or time-varying delays is studied. An approach combining the Lyapunov-Krasovskii functional with the linear matrix inequality (LMI) is taken to study the problem. Some a criteria for the global robust asymptotic stability, which gives information on the delay-dependent property, are derived. Some illustrative examples are given to demonstrate the effectiveness of the obtained results.